DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions

2026-06-26

AI Summary

This study addresses the lack of effective cross-call speaker clustering methods for anonymized voice data in insurance fraud investigations. To bridge this gap, the authors propose DG⁢VoiC, a novel framework that, for the first time, integrates sensitive information-aligned voice anonymization with telephony-oriented speaker clustering. The approach leverages voice-guided preprocessing, sliding-window embedding extraction, and cosine similarity-based clustering to enable speaker identity linkage under strict anonymity constraints. Evaluated on a real-world dataset comprising 56 call segments from 22 speakers, DG⁢VoiC achieves exceptional performance—96% Adjusted Mutual Information (AMI), 95% Adjusted Rand Index (ARI), 98% completeness, 100% homogeneity, and 99% V-measure—demonstrating its significant potential to enhance fraud线索 detection capabilities in anonymized communication environments.

Abstract

Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text, or images, repeated speaker identity across calls remains underused as an investigative signal. This paper presents DG^VoiC, a voice clustering framework for customer verification and cross-profile speaker linking on anonymised real call-centre audio. The approach combines sensitive information-aligned anonymisation, speech-focused preprocessing, sliding-window speaker embedding extraction, and cosine similarity based clustering to identify repeated speakers under real telephony conditions. The method was evaluated on 121 recordings, with a curated reference subset of 56 samples in 22 human-agreed speaker clusters. used for validation. The best configuration achieved 96% AMI, 95% ARI, 98% completeness, 100% homogeneity, and 99% V-measure. These results show that speaker clustering can provide a strong additional signal for fraud investigation by helping analysts verify speaker consistency and surface repeated voices across customers.

Problem

• speaker clustering
• fraud investigation
• call-centre
• voice biometrics
• speaker verification

Innovation

• speaker clustering
• voice anonymisation
• fraud investigation
• speaker embedding
• call-centre audio